{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:4LCO6OMWWKGO4BWAFW646YJYQF","short_pith_number":"pith:4LCO6OMW","canonical_record":{"source":{"id":"1906.00050","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-31T20:01:43Z","cross_cats_sorted":[],"title_canon_sha256":"bade2d2346b7b725a3f6e267afe2dd87bfae857d9e0f52777f8f2730b8d377c8","abstract_canon_sha256":"3b123b89fc545017510c9afae8fe23b22254f7c9c60c0d07aff267bb2084cf86"},"schema_version":"1.0"},"canonical_sha256":"e2c4ef3996b28cee06c02dbdcf61388150fe58bdc06c4ef2bb7d9307d38cf8d4","source":{"kind":"arxiv","id":"1906.00050","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.00050","created_at":"2026-05-17T23:44:33Z"},{"alias_kind":"arxiv_version","alias_value":"1906.00050v1","created_at":"2026-05-17T23:44:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00050","created_at":"2026-05-17T23:44:33Z"},{"alias_kind":"pith_short_12","alias_value":"4LCO6OMWWKGO","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"4LCO6OMWWKGO4BWA","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"4LCO6OMW","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:4LCO6OMWWKGO4BWAFW646YJYQF","target":"record","payload":{"canonical_record":{"source":{"id":"1906.00050","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-31T20:01:43Z","cross_cats_sorted":[],"title_canon_sha256":"bade2d2346b7b725a3f6e267afe2dd87bfae857d9e0f52777f8f2730b8d377c8","abstract_canon_sha256":"3b123b89fc545017510c9afae8fe23b22254f7c9c60c0d07aff267bb2084cf86"},"schema_version":"1.0"},"canonical_sha256":"e2c4ef3996b28cee06c02dbdcf61388150fe58bdc06c4ef2bb7d9307d38cf8d4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:33.619551Z","signature_b64":"x7aDfcD55PGvorqhjbj/ldflRcAW1JsUXL8wPsKFAqj15TgPxR//tbMye5GZll7U9J8MKU2w2xJ1uFWHkaZqBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2c4ef3996b28cee06c02dbdcf61388150fe58bdc06c4ef2bb7d9307d38cf8d4","last_reissued_at":"2026-05-17T23:44:33.618790Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:33.618790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.00050","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C16LIcZW9LOcm4D5YmrSIFdjEJwBkFaCBUVqCgL7MCMWuuIuG75ZJYUHlAdhrrXBk9vxGQ4ka5yfTqN5wHs+DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T15:41:13.580166Z"},"content_sha256":"0e53d97f352482113525eb302d7aa944281aa309558cbeb4a1f6544d62862592","schema_version":"1.0","event_id":"sha256:0e53d97f352482113525eb302d7aa944281aa309558cbeb4a1f6544d62862592"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:4LCO6OMWWKGO4BWAFW646YJYQF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DISCO: Depth Inference from Stereo using Context","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kaushik Raghavan, Kunal Swami, Nikhilanj Pelluri, Pankaj Bajpai, Rituparna Sarkar","submitted_at":"2019-05-31T20:01:43Z","abstract_excerpt":"Recent deep learning based approaches have outperformed classical stereo matching methods. However, current deep learning based end-to-end stereo matching methods adopt a generic encoder-decoder style network with skip connections. To limit computational requirement, many networks perform excessive down sampling, which results in significant loss of useful low-level information. Additionally, many network designs do not exploit the rich multi-scale contextual information. In this work, we address these aforementioned problems by carefully designing the network architecture to preserve required"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00050","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"emM9vKg44kLFxjmhRKOFWu00PLaXXmhJ3oX9sZn1nJ7/LZ2fz3Mx40HPeCQ//jGLJ1JVMV2LbPVctho2ANP0CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T15:41:13.580532Z"},"content_sha256":"ab2c3d72bd671ca6c6f2642811d068502fa70c2b649a972d83b7d7515916dccf","schema_version":"1.0","event_id":"sha256:ab2c3d72bd671ca6c6f2642811d068502fa70c2b649a972d83b7d7515916dccf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4LCO6OMWWKGO4BWAFW646YJYQF/bundle.json","state_url":"https://pith.science/pith/4LCO6OMWWKGO4BWAFW646YJYQF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4LCO6OMWWKGO4BWAFW646YJYQF/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-20T15:41:13Z","links":{"resolver":"https://pith.science/pith/4LCO6OMWWKGO4BWAFW646YJYQF","bundle":"https://pith.science/pith/4LCO6OMWWKGO4BWAFW646YJYQF/bundle.json","state":"https://pith.science/pith/4LCO6OMWWKGO4BWAFW646YJYQF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4LCO6OMWWKGO4BWAFW646YJYQF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4LCO6OMWWKGO4BWAFW646YJYQF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3b123b89fc545017510c9afae8fe23b22254f7c9c60c0d07aff267bb2084cf86","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-31T20:01:43Z","title_canon_sha256":"bade2d2346b7b725a3f6e267afe2dd87bfae857d9e0f52777f8f2730b8d377c8"},"schema_version":"1.0","source":{"id":"1906.00050","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.00050","created_at":"2026-05-17T23:44:33Z"},{"alias_kind":"arxiv_version","alias_value":"1906.00050v1","created_at":"2026-05-17T23:44:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00050","created_at":"2026-05-17T23:44:33Z"},{"alias_kind":"pith_short_12","alias_value":"4LCO6OMWWKGO","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"4LCO6OMWWKGO4BWA","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"4LCO6OMW","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:ab2c3d72bd671ca6c6f2642811d068502fa70c2b649a972d83b7d7515916dccf","target":"graph","created_at":"2026-05-17T23:44:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Recent deep learning based approaches have outperformed classical stereo matching methods. However, current deep learning based end-to-end stereo matching methods adopt a generic encoder-decoder style network with skip connections. To limit computational requirement, many networks perform excessive down sampling, which results in significant loss of useful low-level information. Additionally, many network designs do not exploit the rich multi-scale contextual information. In this work, we address these aforementioned problems by carefully designing the network architecture to preserve required","authors_text":"Kaushik Raghavan, Kunal Swami, Nikhilanj Pelluri, Pankaj Bajpai, Rituparna Sarkar","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-31T20:01:43Z","title":"DISCO: Depth Inference from Stereo using Context"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00050","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0e53d97f352482113525eb302d7aa944281aa309558cbeb4a1f6544d62862592","target":"record","created_at":"2026-05-17T23:44:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3b123b89fc545017510c9afae8fe23b22254f7c9c60c0d07aff267bb2084cf86","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-31T20:01:43Z","title_canon_sha256":"bade2d2346b7b725a3f6e267afe2dd87bfae857d9e0f52777f8f2730b8d377c8"},"schema_version":"1.0","source":{"id":"1906.00050","kind":"arxiv","version":1}},"canonical_sha256":"e2c4ef3996b28cee06c02dbdcf61388150fe58bdc06c4ef2bb7d9307d38cf8d4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e2c4ef3996b28cee06c02dbdcf61388150fe58bdc06c4ef2bb7d9307d38cf8d4","first_computed_at":"2026-05-17T23:44:33.618790Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:33.618790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x7aDfcD55PGvorqhjbj/ldflRcAW1JsUXL8wPsKFAqj15TgPxR//tbMye5GZll7U9J8MKU2w2xJ1uFWHkaZqBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:33.619551Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.00050","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0e53d97f352482113525eb302d7aa944281aa309558cbeb4a1f6544d62862592","sha256:ab2c3d72bd671ca6c6f2642811d068502fa70c2b649a972d83b7d7515916dccf"],"state_sha256":"0c18d64d8485c0e5bf5270b1444d7c5cd4c05b4fff84f179abee1edfeb705d10"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DfsCgRPMfMfHq/+1dkz7iTBP5vFI6npFFbVjxveRnWlMS8vUV+b+2x4Wz9MqYUZnyfCny+jCQGWGA+hd/TDlCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T15:41:13.582388Z","bundle_sha256":"bc3b899263e7696b3b29b41f4e16b43262f09f6223f022117e7b5b8ef5994b98"}}